A talk I'll be giving at the AMNH

Here’s a talk I’ll be giving about the meteR package at the AMNH:

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meteR: the maximum entropy theory of ecology in R

29 September 2017

Maximum Entropy

Maximum Entropy and Macroecology

The core of METE

Ecosystem structure function (ESF) Species abundance and metabolic rate distributions follow

Spatial structure function (SSF) Spatial abundance distribution and species area relationship follow

Why theory?

meteR

install.packages('meteR') # we're live on CRAN!
devtools::install_github('cmerow/meteR') # dev version
library(meteR)

meteR: Data + Core

data(arth)
head(arth, n = 3)

##        spp count   mass
## 1 blacchel     1 4.7480
## 2 mecyocul     1 1.6490
## 3 eurynsp1     1 0.2584

meteR: Data + Core

data(arth)
head(arth, n = 3)

##        spp count   mass
## 1 blacchel     1 4.7480
## 2 mecyocul     1 1.6490
## 3 eurynsp1     1 0.2584

arthESF <- meteESF(spp = arth$spp, abund = arth$count, 
                   power = arth$mass^0.75)
arthESF # this is the print method

## METE object with state variables:
##       S0       N0       E0 
##    76.00   547.00 15868.26 
## 
## with Lagrange multipliers:
##         la1         la2 
## 0.037929267 0.004960427

meteR: Data + Core

length(unique(arth$spp))

## [1] 76

sum(arth$count)

## [1] 547

sum(arth$mass^0.75 / min(arth$mass^0.75))

## [1] 15868.26

meteR: Data + Core

length(unique(arth$spp))

## [1] 76

sum(arth$count)

## [1] 547

sum(arth$mass^0.75 / min(arth$mass^0.75))

## [1] 15868.26

arthESF_stateVar <- meteESF(S0 = 76, N0 = 547, E0 = 15868.26)

meteR: Data + Core

data(anbo)
head(anbo, n = 3)

##   row column    spp count
## 1   3      3   cabr     3
## 2   3      3 caspi1    20
## 3   3      3   crcr     3

meteR: Data + Core

data(anbo)
head(anbo, n = 3)

##   row column    spp count
## 1   3      3   cabr     3
## 2   3      3 caspi1    20
## 3   3      3   crcr     3

anboSSF <- meteSSF(spp = anbo$spp, sppID = 'gnwe', 
                   abund = anbo$count, row = anbo$row, 
                   col = anbo$column, A = 1, A0 = 16)
anboSSF

## METE object with state variables:
## n0  A A0 
## 41  1 16 
## 
## with Lagrange multipliers:
## [1] 0.3294792

meteR: Prediction + Analysis

arthSAD <- sad(arthESF)

meteR: Prediction + Analysis

arthSAD <- sad(arthESF)
plot(arthSAD, ptype = 'rad')

meteR: Prediction + Analysis

arthSAD <- sad(arthESF)
plot(arthSAD, ptype = 'rad')

logLik(arthSAD)

## 'log Lik.' -201.8189 (df=2)

meteR: Prediction + Analysis

anboSAR <- meteSAR(spp = anbo$spp, abund = anbo$count, 
                   row = anbo$row, col = anbo$col, 
                   Amin = 1, A0 = 16)
plot(anboSAR)

meteR: Prediction + Analysis

anboSAR <- meteSAR(spp = anbo$spp, abund = anbo$count, 
                   row = anbo$row, col = anbo$col, 
                   Amin = 1, A0 = 16)
plot(anboSAR)

mse(anboSAR)

## [1] 29.79903

The Future

pika package to integrate quantitative macroecology
- standardized goodness of fit
- more than just SAD, SAR, metabolism
- will be on CRAN and module in Wallace
meteR (and/or pika) to facilitate self-publishing of data and analyses

Thanks!

ajrominger.github.io